FeatureLanguage: Automatic Generation of Application Backend for Model-Based Programming Course Projects
Bibliographic record
Abstract
University programs in software engineering or computer science increasingly include foundational courses in model-driven engineering. Building a substantial application through a term-long group project is one hands-on, practical way to learn the concepts taught in these courses. While learning by example can be very beneficial, providing students of these model-based programming courses with solutions in the form of complete working applications can be a real challenge due to time and resource constraints. In this paper, we argue that by specifying high-level requirements using our FeatureLanguage, we can completely generate the backend (i.e., Controller and Model) of a Model-View-Controller (MVC) application suitable for a university-level course. The proposed FeatureLanguage is an extension of a domain model with a specification of the different features the application should be able to accommodate as well as the constraints that need to be enforced. First, we discuss the FeatureLanguage, followed by an explanation of the different transformations from the FeatureLanguage to the backend code. We demonstrate that the complete backend can be generated and compare a generated MVC application with its handwritten counterpart. We argue that it is also feasible to completely generate a Controller test suite following a behaviour-driven development approach as well as the frontend of the MV C application, which we will explore in future work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".